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gsr_data_analysis

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gsr_data_analysis [2010/06/17 20:40] – created jochengsr_data_analysis [2010/06/18 23:22] (current) – changed onsets to be in ms jochen
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 <code matlab GSR_data_readacq.m>% use file selector of xff class <code matlab GSR_data_readacq.m>% use file selector of xff class
-gsr = xff('*.acq', 'Please select the GSR-ACQ file to work with...');+gsr = xff('*.acq', 'Please select the GSR-ACQ file to work with...');</code>
  
-% the actual data will then either be in +To use a variable in a MAT file, simply use the ''load MATFILE'' call in Matlab: 
-data = gsr.RawData(CHANNEL, :)'; +<code matlab GSR_data_loadmat.m>% load a mat file (e.g. an ACQ->MAT converted file) 
- +load HPS1344_session1_GSR.mat; 
-% or in +  
-data = gsr.Channel(CHANNEL).Data;</code> +% create new NTT (used for methods on data!) 
- +ntt = xff('new:ntt'); 
-To use a variable in a MAT file, simply use the ''load MATFILE'' call in Matlab.+  
 +% store data from mat file in ntt 
 +ntt.Data = data;</code>
  
 Or if you require to load the data from a text-based (e.g. log) file, you can use the following code: Or if you require to load the data from a text-based (e.g. log) file, you can use the following code:
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 {{:gsr_example_5secsraw.png|10 seconds of raw GSR data recording}} {{:gsr_example_5secsraw.png|10 seconds of raw GSR data recording}}
  
-As you can see, the very small signal variations are normally quite uninteresting (as the skin response only very sluggishly follows the stimulation/emotional state of the subject). Hence, it is usually a good idea to resample the signal to a more suitable resolution. To keep some of the finer details (and being able to determine latency values with a reasonable resolution), for this example I resampled the data to 100Hz using the [[resampleaa]] function (which works a little better than using just ''downsample''):+As you can see, the very small signal variations are normally quite uninteresting (as the skin response only very sluggishly follows the stimulation/emotional state of the subject). Hence, it is usually a good idea to resample the signal to a more suitable resolution. To keep some of the finer details (and being able to determine latency values with a reasonable resolution), for this example I resampled the data to 100Hz using the [[acq.Resample|ACQ::Resample]] or [[ntt.Resample|NTT::Resample]] method (internally using the [[resampleaa]] function which works a little better than using just ''downsample''):
  
 <code matlab GSR_resampledplot.m>% resampling the data <code matlab GSR_resampledplot.m>% resampling the data
-rdata = resampleaa(data, 5);+% the first channel is actual physiological data and needs hi-quality 
 +% resampling (cubic/gaussian average), the stim channels 2:9 
 +% only need indexing into the correct temporal samples 
 +gsr.Resample(100, struct('cubic', [true, false(1, 8)]));
  
 % plotting same time index with stronger line % plotting same time index with stronger line
-set(plot(rdata(50400:51399)), 'LineWidth', 2);</code>+data = gsr.ChannelData(1); 
 +set(plot(data(50400:51399)), 'LineWidth', 2);</code>
  
 {{:gsr_example_5secsresampled.png|100Hz-resampled GSR data}} {{:gsr_example_5secsresampled.png|100Hz-resampled GSR data}}
  
 ===== (Pre-) Filtering data ===== ===== (Pre-) Filtering data =====
-While a lot of the very high-frequency noise is (naturally) gone after the resampling, there is still a lot of signal variation that is (very likely) unrelated to any task/stimulation the subject could have reacted to. Further the signal pre-processing, I then applied a difference (first discreet derivative, ''diff'')-filter that removes artifacts in signals which are unlikely to be in the desired spectrum using the [[prefilter]] function:+While a lot of the very high-frequency noise is (naturally) gone after the resampling, there is still a lot of signal variation that is (very likely) unrelated to any task/stimulation the subject could have reacted to. Further the signal pre-processing, I then applied a difference (first discreet derivative, ''diff'')-filter that removes artifacts in signals which are unlikely to be in the desired spectrum using the [[acq.Filter|ACQ::Filter]]/[[ntt.Filter|NTT::Filter]] method (which makes use of the [[prefilter]] function):
  
-<code matlab GSR_prefilter.m>% simply pass to prefilter +<code matlab GSR_prefilter.m>% call obj.Filter with default parameters for first channel 
-fdata = prefilter(rdata);+gsr.Filter(1);
  
 % plotting same time index with stronger line % plotting same time index with stronger line
-set(plot(fdata(50400:51399)), 'LineWidth', 2);</code>+data = gsr.ChannelData(1); 
 +set(plot(data(50400:51399)), 'LineWidth', 2);</code>
  
 {{:gsr_example_5secsfiltered.png|filtered GSR data}} {{:gsr_example_5secsfiltered.png|filtered GSR data}}
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 <code matlab GSR_acq_stimonsets.m>% all stimuli onsets (use 1 + to cope for the fact that the <code matlab GSR_acq_stimonsets.m>% all stimuli onsets (use 1 + to cope for the fact that the
 % first value in diff rather codes the second sample in the data! % first value in diff rather codes the second sample in the data!
-all_onsets = 1 + find(diff(gsr.RawData(8, :)') > 0);+all_onsets = 1 + find(diff(gsr.ChannelData(8)) > 0);
  
 % now split into three conditions % now split into three conditions
-neu = all_onsets(gsr.RawData(2, all_onsets) > 0); +neu = 0.01 * all_onsets(gsr.ChannelData(2, all_onsets) > 0); 
-neg = all_onsets(gsr.RawData(3, all_onsets) > 0); +neg = 0.01 * all_onsets(gsr.ChannelData(3, all_onsets) > 0); 
-rea = all_onsets(gsr.RawData(4, all_onsets) > 0); +rea = 0.01 * all_onsets(gsr.ChannelData(4, all_onsets) > 0);</code>
- +
-% cope for resampling from 500Hz -> 100Hz !! +
-neu = round(0.2 .* neu); +
-neg = round(0.2 .* neg); +
-rea = round(0.2 .* rea);</code>+
  
 As window length for the inspection of the GSR data, we choose 15 seconds, so in the resolution of the resampled data that means 1,500 samples. As window length for the inspection of the GSR data, we choose 15 seconds, so in the resolution of the resampled data that means 1,500 samples.
 +
 +In our second example, the data only contains the onsets (not the condition information). This has to be established separately (which can be even done **after** extracting the peak-to-peak values, thus allowing for a blind-analysis of data, as the examiner doesn't know which onset belongs to which specific condition):
 +
 +<code matlab GSR_plain_onsets.m>% loading the data
 +% (in this case a MAT file written by Acqknowledge for ACQ Version >= 4)
 +% which is in 200Hz sampling frequency with
 +% GSR = channel 1 and
 +% ECG = channel 2
 +load 6701.acq.mat
 +
 +% the stimulus channel in this example is channel 3,
 +% coded with 0 Volts for no stimulus and 5V for stimulus
 +% thus, to find the time points (samples) on which a
 +% stimulus occurs, we look for spots in this channel
 +% where the value changes from below 2.5V to above 2.5V
 +stimactive = (data(:, 3) > 2.5);
 +onsets = find(~stimactive(2:end) & stimactive(1:end-1));
 +
 +% creating the necessary information for plotcurves
 +% the onset is shifted by 200 samples (1 second) to show
 +% a window of [-1 .. 14] (in seconds)
 +c = cell(numel(onsets), 2);
 +for cc = 1:numel(onsets)
 +    c{cc,1} = sprintf('Onset %d, t=%.3f', cc, onsets(cc)/200);
 +    c{cc, 2} = [1, onsets(cc)-200, onsets(cc)+2800];
 +end
 +
 +% calling plotcurves
 +plotcurves(data, struct('curves', {c}, 'freq', 200));</code>
  
 ===== Peak-to-peak analysis ===== ===== Peak-to-peak analysis =====
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 <code GSR_peaktopeak.m>% create required options argument for plotcurves <code GSR_peaktopeak.m>% create required options argument for plotcurves
 gsrchannel = 1; gsrchannel = 1;
-neucurves = cell(numel(neu), 3); +neucurves = cell(numel(neu), 2); 
-negcurves = cell(numel(neg), 3); +negcurves = cell(numel(neg), 2); 
-reacurves = cell(numel(rea), 3);+reacurves = cell(numel(rea), 2);
 for sc = 1:numel(neu) for sc = 1:numel(neu)
-    neucurves{sc, 1} = sprintf('LookNeu %02d at t=%.3fs', sc, 0.01 * neu(sc)); +    neucurves{sc, 1} = sprintf('LookNeu %02d at t=%.3fs', sc, neu(sc)); 
-    neucurves{sc, 2} = [gsrchannel, neu(sc), neu(sc) + 1499];+    neucurves{sc, 2} = [gsrchannel, neu(sc) - 2, neu(sc) + 14];
 end end
 for sc = 1:numel(neg) for sc = 1:numel(neg)
-    negcurves{sc, 1} = sprintf('LookNeg %02d at t=%.3fs', sc, 0.01 * neg(sc)); +    negcurves{sc, 1} = sprintf('LookNeg %02d at t=%.3fs', sc, neg(sc)); 
-    negcurves{sc, 2} = [gsrchannel, neg(sc), neg(sc) + 1499];+    negcurves{sc, 2} = [gsrchannel, neg(sc) - 2, neg(sc) + 14];
 end end
 for sc = 1:numel(rea) for sc = 1:numel(rea)
-    reacurves{sc, 1} = sprintf('ReaNeg %02d at t=%.3fs', sc, 0.01 * rea(sc)); +    reacurves{sc, 1} = sprintf('ReaNeg %02d at t=%.3fs', sc, rea(sc)); 
-    reacurves{sc, 2} = [gsrchannel, rea(sc), rea(sc) + 1499];+    reacurves{sc, 2} = [gsrchannel, rea(sc) - 2, rea(sc) + 14];
 end end
 +
 +% spots
 +spot = {[neu; neg; rea]};
 +spotnames = {'Stim onset'};
  
 % combine curves % combine curves
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 % use plot curves % use plot curves
-plotcurves(gsr, struct('curves', {curves}, 'sets', {sets}));</code>+plotcurves(gsr, struct( ... 
 +    'curves',    {curves}, ... 
 +    'sets',      {sets}, ... 
 +    'spot',      {spot}, ... 
 +    'spotnames', {spotnames}));</code>
  
gsr_data_analysis.1276807240.txt.gz · Last modified: 2010/06/17 20:40 by jochen